--- title: "U.S. National Parks Visits (1904-2016)" description: "Tidy Tuesday: Week 38, 2019" author: - name: Mickey Rafa url: https://mrafa3.github.io/ date: 12-30-2022 categories: [R, "#TidyTuesday", bump-chart, gt, web-scraping] # self-defined categories image: plot_viz_538.png draft: false # setting this to `true` will prevent your post from appearing on your listing page until you're ready! format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true editor_options: chunk_output_type: inline execute: error: false message: false warning: false eval: true --- # 1. Load Packages & Setup ```{r setup, include=TRUE} if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, tidytuesdayR, dlookr, ggtext, gt, gtExtras, #for font awesome icons in gt tables ggbump, showtext, janitor, #for clean_names() scales, htmltools, #for tagList() glue, here, geomtextpath ) font_add('fa-brands', 'fonts/Font Awesome 6 Brands-Regular-400.otf') sysfonts::font_add_google("Lato","lato") showtext::showtext_auto() showtext::showtext_opts(dpi=300) ``` # 2. Read in the Data ```{r read_data, include=TRUE} tuesdata <- tidytuesdayR::tt_load(2019, week = 38) df <- tuesdata$national_parks ``` # 3. Examine the Data ```{r glimpse, include=TRUE} df %>% glimpse() ``` # 4. Tidy the Data ```{r df_annual} df_annual <- df %>% filter(year != 'Total', unit_type == 'National Park') %>% mutate(year = as.numeric(year)) %>% mutate(decade = as.factor(year - (year %% 10))) %>% arrange(year) %>% group_by(year) %>% mutate(annual_visitor_rank = as.integer(rank(-visitors))) %>% select(annual_visitor_rank, year, unit_name, visitors, everything()) %>% arrange(year, annual_visitor_rank) %>% ungroup() %>% separate(col=unit_name, into = c("parkname_full", "parktype"), sep = "National Park", remove=FALSE) ``` ```{r highlight_parks, include=TRUE} highlight_list_annual <- df_annual %>% filter(year ==2016, annual_visitor_rank <=5) %>% pull(unit_name) ``` ```{r df_decade, include=TRUE} df_decade <- df %>% filter(unit_type == 'National Park', year != 'Total') %>% mutate(year = as.numeric(year)) %>% mutate(decade = year - (year %% 10)) %>% group_by(decade, unit_name, .groups=TRUE) %>% summarise(visitors_by_decade = sum(visitors, na.rm = TRUE)) %>% ungroup() %>% group_by(decade) %>% mutate(rank_visitors_by_decade = as.integer(rank(-visitors_by_decade))) %>% ungroup() %>% separate(col=unit_name, into = c("parkname_full", "parktype"), sep = "National Park", remove=FALSE) top_1900s <- df_decade %>% filter(decade == 1900) %>% arrange(rank_visitors_by_decade) %>% head(5) %>% pull(unit_name) top_2010s <- df_decade %>% filter(decade == 2010) %>% arrange(rank_visitors_by_decade) %>% head(5) %>% pull(unit_name) ``` # 5. Visualization Parameters ```{r my_theme, include=TRUE} my_theme <- theme( text = element_text(family = 'lato'), plot.title = element_textbox_simple(color="black", face="bold", size=20, hjust=0), plot.subtitle = element_textbox_simple(color="black", size=12, hjust=0), axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), axis.line = element_blank(), plot.caption = element_textbox_simple(color="black", size=12), panel.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(), legend.title=element_blank(), legend.text = element_text(color="black", size=12, hjust=0), legend.position = 'top', strip.text = element_text(color="black", size=14)) title <- tagList(p('Ranking of popularity of U.S. National Parks')) subtitle <- tagList(span('*by the number of visitors annually*')) caption <- paste0("<span style='font-family:lato;'>**Source**: TidyTuesday Week 38 (2019)</span><br>", "<span style='font-family:fa-brands;'></span>", "<span style='font-family:lato;'>@mickey.rafa</span>", "<span style='font-family:lato;color:white;'>....</span>", "<span style='font-family:fa-brands;'></span>", "<span style='font-family:lato;color:white;'>.</span>", "<span style='font-family:lato;'>mrafa3</span>") color_palette <- viridisLite::mako(8) description_color <- 'grey40' subtitle_3 <- tagList(span('*by the number of visitors by decade*')) ``` # 6. Plot ```{r plot_viz_538, include=TRUE, fig.align='center'} (plot_viz_538 <- df_annual %>% ggplot(., aes(x=year, y=-annual_visitor_rank, group=unit_name, color=unit_name)) + geom_line(color='gray80') + geom_line(data=. %>% filter(unit_name %in% highlight_list_annual)) + ggrepel::geom_text_repel( data = df_annual %>% filter(year == 2016, unit_name %in% highlight_list_annual), aes(label = paste("#", annual_visitor_rank, parkname_full)), nudge_x = 15, size = 3, direction = 'y', fontface = 'bold' ) + labs(x='', title = title, subtitle = subtitle, caption = caption) + coord_cartesian(xlim = c(1900, 2040), ylim = c(-65, 2), expand = F) + my_theme + theme(legend.position = 'none')) ``` ```{r viz_decade_bump, include=TRUE, fig.align='center'} (plot_viz_decade_bump <- df_decade %>% filter(unit_name %in% c(top_1900s, top_2010s), decade >= 1900) %>% ggplot(., aes(x=decade, y=-rank_visitors_by_decade, col=unit_name)) + geom_point(shape = '|', stroke = 6) + geom_bump(linewidth = 1) + ggrepel::geom_text_repel( data = df_decade %>% filter(decade == 1900, unit_name %in% top_1900s), aes(label = paste('#',rank_visitors_by_decade, " ", parkname_full, sep = "")), #nudge_x = -1, hjust = 1, size = 4, direction = "y", fontface = 'bold' ) + ggrepel::geom_text_repel( data = df_decade %>% filter(decade == 2010, unit_name %in% top_2010s), aes(label = paste('#',rank_visitors_by_decade, " ", parkname_full, sep = "")), hjust = 0, nudge_x = 1, size = 4, direction = "y", fontface = 'bold' ) + geom_text( data = df_decade %>% filter(decade == 2010, unit_name %in% c('Hot Springs National Park', 'Wind Cave National Park', 'Crater Lake National Park')), aes(label = paste('#',rank_visitors_by_decade, " ", parkname_full, sep = "")), hjust = 0, nudge_x = 1, size = 4, fontface = 'bold' ) + annotate( 'text', x = c(1898, 2012), y = c(5, 5), label = c('1900s', '2010s'), hjust = c(0, 1), vjust = 1, size = 6, fontface = 'bold') + coord_cartesian(xlim = c(1860, 2070), ylim = c(-45, 10), expand = F) + #theme_void() + my_theme + theme(legend.position = 'none', panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), #plot.background = element_rect(fill = color_palette[8], color = NA), text = element_text( color = description_color ) ) + labs( title = title, subtitle = subtitle_3, caption = caption )) ``` # 2010 to present ```{r} <- df %>% filter (year != 'Total' ) %>% mutate (year = as.numeric (year)) %>% filter (year >= 2010 ,! unit_type %in% c ('Parkway' , 'National Parkway' )) %>% group_by (year) %>% mutate (rank_annual_visitors = as.integer (rank (- visitors))) %>% arrange (year, rank_annual_visitors))``` ```{r} <- df_recent_all_types %>% ungroup () %>% filter (rank_annual_visitors <= 6 ) %>% distinct (unit_name) %>% pull ())``` ```{r} %>% filter (rank_annual_visitors <= 6 ) %>% ggplot (.,aes (x= year, y= rank_annual_visitors,col= unit_name)) + geom_point (shape = '|' , stroke = 6 ) + geom_bump (size = 1 ) + geom_text (data = df_recent_all_types %>% filter (year == 2010 , unit_name %in% highlight_df_recent_all_types),#should string manip the labels to cut out National Park from name aes (label = unit_name),size = 3 ,hjust = 1 ,nudge_x = - 0.1 ,fontface = 'bold' + geom_text (data = df_recent_all_types %>% filter (year == 2016 , unit_name %in% highlight_df_recent_all_types),aes (label = rank_annual_visitors),hjust = 0 ,nudge_x = 1 ,size = 5 ,fontface = 'bold' + annotate ('text' ,x = c (2010 , 2016 ),y = c (0.25 , 0.25 ),label = c ('2010' , '2016' ),hjust = c (0 , 1 ),vjust = 1 ,size = 4 ,fontface = 'bold' ,color = description_color) + scale_y_reverse (position = 'right' , breaks = seq (20 , 2 , - 2 )) + #scale_color_manual(values = country_colors) + coord_cartesian (xlim = c (2005 , 2020 ), ylim = c (12 , 0 ), expand = F) + #theme_void() + + theme (legend.position = 'none' ,panel.grid.major.x = element_blank (),panel.grid.minor.x = element_blank (),#plot.background = element_rect(fill = color_palette[8], color = NA), text = element_text (color = description_color+ labs (title = 'Ranking the popularity of U.S. National Parks' ,subtitle = 'by total visitors each decade' ``` # Scraping Deferred Maintainance ```{r read_maintenance_url, include=TRUE} url <- 'https://www.nps.gov/common/uploads/sortable_dataset/infrastructure/9143F3CC-B3B8-3F78-2F70BE310E528B37/nri-DMParkFactSheettoConverttoaCSV-Copy.csv?t=1662071341782' df_maintenance <- read_csv(url) %>% clean_names() %>% mutate(deferred_maintenance = as.numeric(gsub('[$,]', '', dm_r))) %>% select(park_name, deferred_maintenance) ``` ```{r scatter_maintenance_visitors, include=FALSE} df_recent_all_types %>% filter(year == 2016) %>% left_join(x=., y=df_maintenance, by=c('unit_name' = 'park_name')) %>% ggplot(., aes(x=visitors, y=deferred_maintenance)) + geom_point() + #geom_smooth(stat = 'lm') + #geom_text(data=. %>% filter(integer(rank(deferred_maintenance)) <= 5), # aes(label=unit_name)) + my_theme ``` ## Table ```{r} <- df_annual %>% group_by (unit_name) %>% mutate (year_opened = min (year)) %>% ungroup () %>% select (year, unit_name, visitors, state, year_opened) %>% filter (year %in% c (2006 , 2016 )) %>% spread (year, visitors) %>% mutate (percent_change_2006_2016 = (` 2016 ` - ` 2006 ` )/ ` 2006 ` ,rank_visitors_2006 = as.integer (rank (- ` 2006 ` )),rank_visitors_2016 = as.integer (rank (- ` 2016 ` )),rank_change_2016 = rank_visitors_2006 - rank_visitors_2016) %>% select (unit_name, state, year_opened, rank_visitors_2016, visitors= ` 2016 ` , %>% left_join (x= .,y= df_maintenance,by= c ('unit_name' = 'park_name' )) %>% ungroup () %>% mutate (deferred_maintenance = round (deferred_maintenance / 1000000 , 0 )) %>% arrange (- visitors) %>% slice (1 : 15 )``` ```{r min_max_palette, include=TRUE} min_visitors <- df_tbl$visitors %>% min() max_visitors <- df_tbl$visitors %>% max() visitors_palette <- col_numeric(c("#e5f5e0", "#31a354"), domain = c(min_visitors, max_visitors), alpha = .75) max_deferred <- df_tbl$deferred_maintenance %>% max() min_deferred <- df_tbl$deferred_maintenance %>% min() deferred_palette <- col_numeric(c("#fee0d2", "#de2d26"), domain = c(min_deferred, max_deferred), alpha = .75) ``` ```{r tbl_viz, include=TRUE} (tbl_viz <- df_tbl %>% gt() %>% #rename columns cols_label(rank_visitors_2016 = 'Rank Visitors', unit_name = 'Park', visitors = 'Visitors', percent_change_2006_2016 = '% Change', rank_change_2016 = 'Rank Change', state = 'State', year_opened = 'Year Opened', deferred_maintenance = 'Deferred Maintenance & Repairs Est. (2021, Millions)') %>% #format numeric columns fmt_number(columns = c(visitors), sep_mark = ",", decimals = 0) %>% fmt_percent(columns = c(percent_change_2006_2016), decimals = 0) %>% fmt_currency(columns = c(deferred_maintenance), decimals = 0) %>% #add tab spanners tab_spanner( label = md('**2006 - 2016**'), columns = c(percent_change_2006_2016, rank_change_2016) ) %>% tab_spanner( label = md('**2016**'), columns = c(rank_visitors_2016, visitors) ) %>% #add table title tab_header(title = md("**Most Popular U.S. National Parks in 2016**"), subtitle = 'by estimated number of visitors') %>% tab_source_note(source_note = md("Visitor data sourced from Tidy Tuesday Week 38 (2019)<br>Deffered Maintenance & Repairs estimates sourced from nps.gov")) %>% #apply new style to all column headers tab_style( locations = cells_column_labels(columns = everything()), style = list( #thick border cell_borders(sides = "bottom", weight = px(3)), #make text bold cell_text(weight = "bold") ) ) %>% #apply different style to title tab_style(locations = cells_title(groups = "title"), style = list( cell_text(weight = "bold", size = 24) )) %>% data_color(columns = c(visitors), colors = visitors_palette) %>% data_color(columns = c(deferred_maintenance), colors = deferred_palette) %>% opt_all_caps() %>% opt_table_font( font = list( google_font("Chivo"), default_fonts() ) ) %>% tab_options( #remove border between column headers and title column_labels.border.top.width = px(3), column_labels.border.top.color = "transparent", #remove border around the table table.border.top.color = "transparent", table.border.bottom.color = "transparent", #adjust font sizes and alignment source_notes.font.size = 12, heading.align = "left" )) ``` # 7. Save # 8. Session Info ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` # 9. Github Repository ##### Expand for GitHub Repo [ Access the GitHub repository here ](https://github.com/mrafa3/mrafa3.github.io) 
